class_docstr: Gated Graph Convolution layer from paper `Gated Graph Sequence
  Neural Networks <https://arxiv.org/pdf/1511.05493.pdf>`__.
  
  .. math::
     h_{i}^{0} = [ x_i \| \mathbf{0} ]
  
     a_{i}^{t} = \sum_{j\in\mathcal{N}(i)} W_{e_{ij}} h_{j}^{t}
  
     h_{i}^{t+1} = \mathrm{GRU}(a_{i}^{t}, h_{i}^{t})
  
  Parameters
  ----------
  input_size: int
      Input feature size.
  output_size: int
      Output feature size.
  num_layers: int
      Number of GGNN layers. Default: 1.
  n_etypes: int
      Number of edge types. Default: 1.
  bias: bool
      If True, adds a learnable bias to the output. Default: True.
code: def __init__(
      self, input_size, output_size, num_layers, n_etypes, bias=True
  ):
      super(UndirectedGGNNLayerConv, self).__init__()
      self._in_feats = input_size
      self._out_feats = output_size
      self._num_layers = num_layers
      self._n_etypes = n_etypes
      self.linears = nn.ModuleList(
          [nn.Linear(output_size, output_size) for _ in range(n_etypes)]
      )
      self.gru = nn.GRUCell(output_size, output_size, bias=bias)
      self.reset_parameters()
  
imports: import dgl
  import dgl.function
  import torch
  import torch.nn
  from torch.nn import init
  from ...data.data import GraphData
  from .base import GNNBase, GNNLayerBase
label_desc: Use image node_id 1 to create a new UndirectedGGNNLayerConv object from inherited base classes: GNNLayerBase with example: obj = UndirectedGGNNLayerConv(input_size, output_size, num_layers, n_etypes, bias)